We are ready for clinical implementation of Carbon Ion Radiotherapy in the United States
Bibliographic record
Abstract
In the past three decades, all carbon ion treatment centers are mainly located in Europe and Asia, yet the therapeutic benefits of carbon ion therapy were first discovered in the United States in 1970s.Clinical outcome studies are coming out in the recent years from those centers comparing carbon ion treatment with conventional photon radiotherapy or surgery.1,2 People might have been wondering, as one article published in Wired late last year put it, "Carbon ion radiation therapy (CIRT) is being used to blast tumors all over the world.Just not in the country that invented it.Why a promising, potent cancer therapy isn't used in the US?" 3 The article revealed that University of Texas Southwestern Medical Center hopes to be the front-runner of building a carbon ion treatment facility in the United States, which is estimated to cost $300 million.Just around the same time, Mayo Clinic announced its agreement with Hitachi, Ltd in building the first carbon ion treatment facility at one of Mayo's campuses located in Jacksonville, Florida.Major stakeholders in the cancer treatment field have shown strong interests in building a CIRT facility in the United States.Our previous Parallel Opposed editorial also debated on the need of having at least one carbon ion facility in the country.4 Yet the question remains whether it is clinically and financially ready for construction and implementation of a CIRT facility in the United States.Herein, we have the leading figure for the Mayo Clinic CIRT project, Dr.Chris Beltran, arguing for the proposition that CIRT is ready for clinical implementation in US, and the world-renowned scientist in proton therapy and ion-beam related research, Richard Amos, arguing against the proposition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.090 | 0.023 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".